Researchers and developers increasingly rely on toxicity scoring to moderate generative language model outputs, in settings such as customer service, information retrieval, and content generation. However, toxicity scoring may render pertinent information inaccessible, rigidify or "value-lock" cultural norms, and prevent language reclamation processes, particularly for marginalized people. In this work, we extend the concept of algorithmic recourse to generative language models: we provide users a novel mechanism to achieve their desired prediction by dynamically setting thresholds for toxicity filtering. Users thereby exercise increased agency relative to interactions with the baseline system. A pilot study ($n = 30$) supports the potential of our proposed recourse mechanism, indicating improvements in usability compared to fixed-threshold toxicity-filtering of model outputs. Future work should explore the intersection of toxicity scoring, model controllability, user agency, and language reclamation processes -- particularly with regard to the bias that many communities encounter when interacting with generative language models.

通过为毒性过滤设置动态阈值，我们提供了一种新的机制，使用户能够实现他们希望的预测，从而增加了与基线系统的交互中的机构性。一项初步研究支持我们提出的救济机制的潜力，表明与固定阈值毒性过滤模型输出相比，可用性有所改善。未来的工作应该探讨毒性评分、模型可控性、用户机构性和语言重建过程之间的交叉点，特别是关于当与生成性语言模型互动时，许多社区遇到的偏见。

追索索偿：与生成语言模型对话